Automated breath detection on long-duration signals using feedforward backpropagation artificial neural networks

A new breath-detection algorithm is presented, intended to automate the analysis of respiratory data acquired during sleep. The algorithm is based on two independent artificial neural networks (ANN_insp and ANN_expi) that recognize, in the original signal, windows of interest where the onset of inspiration and expiration occurs. Postprocessing consists in finding inside each of these windows of interest minimum and maximum corresponding to each inspiration and expiration. The ANN_insp and ANN_expi correctly determine respectively 98.0% and 98.7% of the desired windows, when compared with 29 820 inspirations and 29 819 expirations detected by a human expert, obtained from three entire-night recordings. Postprocessing allowed determination of inspiration and expiration onsets with a mean difference with respect to the same human expert of (mean ± SD) 34 ± 71 ms for inspiration and 5 ± 46 ms for expiration. The method proved to be effective in detecting the onset of inspiration and expiration in full night continuous recordings. A comparison of five human experts performing the same classification task yielded that the automated algorithm was undifferentiable from these human experts, failing within the distribution of human expert results. Besides being applicable to adult respiratory volume data, the presented algorithm was also successfully applied to infant sleep data, consisting of uncalibrated rib cage and abdominal movement recordings. A comparison with two previously published algorithms for breath detection in respiratory volume signal shows that the presented algorithm has a higher specificity, while presenting similar or higher positive predictive values.